Revisiting Redundancy in Diffusion Transformers: A Temporal-Spatial Joint Caching Strategy for Efficient Sampling

Diffusion Transformers (DiTs) achieve impressive generative performance but suffer from significant inference latency. Feature caching–based acceleration methods reduce total computation by reusing results from earlier timesteps, but they largely ignore that temporal redundancy is dynamic and inconsistent across timesteps. Our analysis reveals this variability. More crucially, we identify a previously underexplored form of efficiency, namely spatial redundancy, characterized by high similarity between adjacent transformer blocks within the same timestep. Motivated by this dual-dimensional redundancy, we propose Temporal-Spatial Joint Cache, a training-free inference acceleration strategy that dynamically determines optimal reuse operations across temporal and spatial dimensions. Our approach features a redundancy-guided operation selector that estimates local feature stability using second-order divided differences, enabling fine-grained decisions between full computation, temporal cache, and spatial cache. Furthermore, we use interpolation-based feature prediction to capture local feature evolution for more accurate reuse. In addition, we propose a bounded cache distance control mechanism to mitigate error accumulation from excessive reuse. Together, these components allow our method to deliver substantial inference speedups without retraining or compromising generation fidelity, offering a new perspective on efficiency in diffusion transformer inference.